Sjgren's Syndrome (SS) is autoinflammatory condition, characterized by a plethora of symptoms, including dry eyes and mouth. Despite the fact that the condition is relatively common, with up to 3.1 million persons affected in the US, accurate diagnosis has been complex and largely ineffective. If left undiagnosed, and thus untreated, SS can progress to significant morbidity and increase the risk of certain forms of cancer up to 100 fold. The identification of SS patients is, therefore, a pressing unmet need. Current SS diagnostic tools have been limited, in part because of the wide range of symptoms and frequent overlap with other autoinflammatory disorders. In this study, we will use genome-wide expression profiling of peripheral blood leukocytes coupled with multivariate analysis to identify SS diagnostic biomarkers of disease. Our preliminary data support the hypothesis that such biomarkers can be identified that correctly classify patients and provide a powerful adjunct to current SS diagnostic methods. Our preliminary data demonstrate that a multi-variate gene expression-based algorithm has superior performance to distinguish among SS, rheumatoid arthritis (RA), and unaffected controls than current diagnostic laboratory tests. In this proposal, an algorithm will be developed utilizing a broader set of samples from additional common rheumatologic diseases within a standard SS differential. Multi-variate algorithm development will be done using state-of-the-art multi-variate statistics, utilizing adequately powered cohorts, and an iterative process of biomarker identification and verification though serial associative analyses in independent cohorts. Our group has utilized this general methodology to create multivariate biomarker algorithms highly correlated to clinical outcome, which have been translated into clinical laboratory tests.